Enhancing Cybersecurity with Encrypted Traffic Fingerprinting




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Recently, network traffic analysis and cyber deception have been increasingly used in various applications to protect people, information, and systems from major cyber threats. Network traffic fingerprinting is a traffic analysis attack which threatens web navigation privacy. It is a set of techniques used to discover patterns from a sequence of network packets generated while a user accesses different websites. Internet users (such as online activists or journalists) may wish to hide their identity and online activity to protect their privacy. Typically, an anonymity network is utilized for this purpose. These anonymity networks such as Tor (The Onion Router) provide layers of data encryption which poses a challenge to the traffic analysis techniques.

Traffic fingerprinting studies have employed various traffic analysis and statistical techniques over anonymity networks. Most studies use a similar set of features including packet size, packet direction, total count of packets, and other summaries of different packets. More-over, various defense mechanisms have been proposed to counteract these feature selection processes, thereby reducing prediction accuracy.

In this dissertation, we address the aforementioned challenges and present a novel method to extract characteristics from encrypted traffic by utilizing data dependencies that occur over sequential transmissions of network packets. In addition, we explore the temporal nature of encrypted traffic and introduce an adaptive model that considers changes in data content over time. We not only consider traditional learning techniques for prediction, but also use semantic vector space models (VSMs) of language where each word (packet) is represented as a real-valued vector. We also introduce a novel defense algorithm to counter the traffic fingerprinting attack. The defense uses sampling and mathematical optimization techniques to morph packet sequences and destroy traffic flow dependency patterns.

Cyber deception has been shown to be a key ingredient in cyber warfare. Cyber security deception is the methodology followed by an organization to lure the adversary into a controlled and transparent environment for the purpose of protecting the organization, disinforming the attacker, and discovering zero-day threats. We extend our traffic fingerprinting work to the cyber deception domain and leverage recent advances in software deception to enhance Intrusion Detection Systems by feeding back attack traces into machine learning classifiers. We present a feature-rich attack classification approach to extract security-relevant network-and system-level characteristics from production servers hosting enterprise web applications.



Data encryption (Computer science), Computer security, Internet users, Cyber intelligence (Computer security), Cyberterrorism, Intrusion detection systems (Computer security)



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